Book Description to Finelybook sorting
Ensemble techniques are used for combining two or more similar or dissimilar machine learning algorithms to create a stronger model. Such a model delivers superior prediction power and can give your datasets a boost in accuracy.
Hands-On Ensemble Learning with R begins with the important statistical resampling methods. You will then walk through the central trilogy of ensemble techniques – bagging, random forest, and boosting – then you’ll learn how they can be used to provide greater accuracy on large datasets using popular R packages. You will learn how to combine model predictions using different machine learning algorithms to build ensemble models. In addition to this, you will explore how to improve the performance of your ensemble models.
By the end of this book, you will have learned how machine learning algorithms can be combined to reduce common problems and build simple efficient ensemble models with the help of real-world examples.Contents
1: INTRODUCTION TO ENSEMBLE TECHNIQUES
2: BOOTSTRAPPING
3: BAGGING
4: RANDOM FORESTS
5: THE BARE BONES BOOSTING ALGORITHMS
6: BOOSTING REFINEMENTS
7: THE GENERAL ENSEMBLE TECHNIQUE
8: ENSEMBLE DIAGNOSTICS
9: ENSEMBLING REGRESSION MODELS
10: ENSEMBLING SURVIVAL MODELS
11: ENSEMBLING TIME SERIES MODELS
12: WHAT’S NEXT?
What You Will Learn
Carry out an essential review of re-sampling methods, bootstrap, and jackknife
Explore the key ensemble methods: bagging, random forests, and boosting
Use multiple algorithms to make strong predictive models
Enjoy a comprehensive treatment of boosting methods
Supplement methods with statistical tests, such as ROC
Walk through data structures in classification, regression, survival, and time series data
Use the supplied R code to implement ensemble methods
Learn stacking method to combine heterogeneous machine learning models
Authors
Prabhanjan Narayanachar Tattar
Prabhanjan Narayanachar Tattar is a lead statistician and manager at the Global Data Insights & Analytics division of Ford Motor Company, Chennai. He received the IBS(IR)-GK Shukla Young Biometrician Award (2005) and Dr. U.S. Nair Award for Young Statistician (2007). He held SRF of CSIR-UGC during his PhD. He has authored books such as Statistical Application Development with R and Python, 2nd Edition, Packt; Practical Data Science Cookbook, 2nd Edition, Packt; and A Course in Statistics with R, Wiley. He has created many R packages